1 00:00:00,000 --> 00:00:03,357 And there's been this pattern where these massive models come out uh they 2 00:00:03,440 --> 00:00:06,357 get distilled uh by open source model 3 00:00:06,440 --> 00:00:10,077 makers and then open source is just a 4 00:00:10,160 --> 00:00:14,077 couple months behind the leading foundational models. Um 5 00:00:14,160 --> 00:00:17,517 and you know, I guess like when the investment was smaller, being a couple 6 00:00:17,600 --> 00:00:21,437 months ahead, uh you know, mattered a lot. But I'm curious now that the 7 00:00:21,520 --> 00:00:24,477 investment is so big um and the models 8 00:00:24,560 --> 00:00:26,757 are capabilities are increasing you 9 00:00:26,840 --> 00:00:28,957 know, fairly dramatically you know, uh 10 00:00:29,040 --> 00:00:32,357 as you go. Um how is this defensible in 11 00:00:32,440 --> 00:00:36,277 the long term if you're just going to have that pattern repeat over and over? 12 00:00:36,360 --> 00:00:37,837 Well, I look at it a little differently. 13 00:00:37,920 --> 00:00:42,357 Like I think that the real investment that we are making is into 14 00:00:42,440 --> 00:00:46,717 that end-to-end co-design, right? Of having a system a system of people, 15 00:00:46,800 --> 00:00:49,957 right? Who are producing this technology, right? A way of working 16 00:00:50,040 --> 00:00:53,277 together and some of this is about how you leverage these massive 17 00:00:53,360 --> 00:00:55,837 supercomputers to produce these models. 18 00:00:55,920 --> 00:01:00,797 Now, it is also the case that it's not as simple as you can take the output of 19 00:01:00,880 --> 00:01:04,717 these models and distill and you have exactly the model of the same capability. 20 00:01:04,800 --> 00:01:07,797 It's just smaller and can run fast. If that were the case, we would just do 21 00:01:07,880 --> 00:01:11,837 that and then we would also have a model that would be, you know, much more uh 22 00:01:11,920 --> 00:01:15,517 easy to serve in in many ways. And of course, there's a lot of art behind 23 00:01:15,600 --> 00:01:17,837 distillation. There's a lot of great things there. But the point that I'm 24 00:01:17,920 --> 00:01:21,757 getting at is that the real thing that we are investing in is the machine that 25 00:01:21,840 --> 00:01:26,197 makes the machine. Now, the at the deployment side, we think a lot about 26 00:01:26,280 --> 00:01:30,077 safeguards. We think a lot about mitigations and we do that for many many 27 00:01:30,160 --> 00:01:32,157 different aspects of how these models 28 00:01:32,240 --> 00:01:37,317 could be misused um in real situations. And that's something 29 00:01:37,400 --> 00:01:40,637 that we have been investing in for many years and we think about that across 30 00:01:40,720 --> 00:01:42,477 areas like cyber or thinking about that 31 00:01:42,560 --> 00:01:48,397 areas like bio that we have a I a long-standing effort that you can see in 32 00:01:48,480 --> 00:01:52,157 our preparedness framework, which is public, about how we approach these 33 00:01:52,240 --> 00:01:55,637 kinds of uses of the model and how we try to make maximize the benefits, 34 00:01:55,720 --> 00:01:59,877 mitigate the risks. And so I think it's a real motion that every piece of what 35 00:01:59,960 --> 00:02:03,117 we do needs to connect to the question of how do we 36 00:02:03,200 --> 00:02:06,357 continue to make progress, but also how do we make these models broadly 37 00:02:06,440 --> 00:02:08,917 available? Because that's something that we really believe in, that we believe 38 00:02:09,000 --> 00:02:13,037 this technology empowers people, and that we want it to benefit people and 39 00:02:13,120 --> 00:02:17,237 lift everyone up. Yeah, but just to go back on that, um 40 00:02:17,320 --> 00:02:19,037 the pricing on this model is, I think, 41 00:02:19,120 --> 00:02:23,277 double the last model, GPT-5.4. 42 00:02:23,360 --> 00:02:27,277 Um and so, from an economics or a business standpoint, the question would 43 00:02:27,360 --> 00:02:30,957 be, you know, let's say you keep on progressing, but because there's been 44 00:02:31,040 --> 00:02:33,757 all this infra- infrastructure that's been put towards 45 00:02:33,840 --> 00:02:36,597 training the models, if open source uh 46 00:02:36,680 --> 00:02:38,637 can deliver not as good performance, but 47 00:02:38,720 --> 00:02:42,317 almost as good, uh and do it cheaper, um 48 00:02:42,400 --> 00:02:44,357 how do you handle that threat? 49 00:02:44,440 --> 00:02:47,637 Well, again, I look at it a little differently. So, first of all, if you 50 00:02:47,720 --> 00:02:51,397 look at our history, which really is not driven by anything 51 00:02:51,480 --> 00:02:54,797 in competition, it's just like our our own sort of 52 00:02:54,880 --> 00:02:57,557 progress and and desire, we have dropped 53 00:02:57,640 --> 00:03:02,277 prices on the same level of intelligence year over year, sometimes by literally a 54 00:03:02,360 --> 00:03:05,237 factor of a hundred. Right? It's like, at least an order of magnitude year over 55 00:03:05,320 --> 00:03:06,997 year, sometimes literally a hundred. 56 00:03:07,080 --> 00:03:09,517 But, the thing that keeps happening, it's real Jevons paradox, where it's 57 00:03:09,600 --> 00:03:12,077 like you lower the cost of something, 58 00:03:12,160 --> 00:03:14,877 way more activity happens, right? And I 59 00:03:14,960 --> 00:03:19,517 think that what we keep seeing is that there are returns to intelligence, 60 00:03:19,600 --> 00:03:23,477 right? That for the kinds of tasks that these models are now capable of doing, 61 00:03:23,560 --> 00:03:26,997 that a little bit more intelligence goes a long way. And I think that is the 62 00:03:27,080 --> 00:03:31,677 story of 5.5, that in some ways, you can almost look at it as like, "Oh, there's 63 00:03:31,760 --> 00:03:34,197 just an incremental improvement in intelligence." But, I think there's 64 00:03:34,280 --> 00:03:37,917 going to be a massive improvement in terms of what people use it for. And by 65 00:03:38,000 --> 00:03:40,997 the way, I actually think that incremental is actually very much an 66 00:03:41,080 --> 00:03:44,757 understatement for this model relative to 5.5. You know, it's a point one 67 00:03:44,840 --> 00:03:48,837 improvement in some ways, um but I think that I that that actually really 68 00:03:48,920 --> 00:03:52,717 undersells the magic that we see within this model, and that the that our early 69 00:03:52,800 --> 00:03:57,320 testers have have really [music] seen in their practical work.